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Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Distributed Sensors GroupGoals, Metrics, and Challenges
-Work in Progress-
PAC/C PI Meeting
November 1 – 3 , 2000
Annapolis, Maryland
Robert Parker
USC/ISI East
Overview
• Who We Are
• Challenge/Approach
• Energy Scavenging
• Hardware Power Baseline
• New Ideas Simulation
• Problems and Problem Owners
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
PAC/C Sensor Group
Chandrakasan Power Aware Wireless Microsensor Networks
Prasanna PacMan
Rabaey Ultra-Low Energy Wireless Sensor and Monitor
Networks
Schott Distributed Sensor Networks
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Sensor Group Top Level Goal
GOAL:Create a tactically significant distributed sensor system capable of
operating indefinitely on energy scavenged from the
environment.
APPROACH:Create a wide-dynamic-range
component base controlled by a system-wide, hierarchical power management system.
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Distributed Sensor Assumptions
• Infrequent Events
• Complex Task
• Involves Multiple Sensors/Modes (Distributed)
• System is Taskable
• Events are Automatically Exfiltrated
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Energy Scavenging
Power (Energy) Density
Batteries (Zinc-Air) 1050 -1560 mWh/cm3
Batteries (rechargeable Lithium) 300 mWh/cm3 (3 - 4 V)
Solar
15 mW/cm2 - direct sun
1mW/cm2 - ave. over 24 hrs.
Vibrations 0.05 - 0.5 mW/cm3
Inertial Human Power
Acoustic Noise
3E-6 mW/cm2 at 75 Db
9.6E-4 mW/cm2 at 100 DbNon-Inertial Human Power 1.8 mW (Shoe inserts)
Nuclear Reaction
80 mW/cm3
1E6m Wh/cm3
One Time Chemical Reaction
Fluid Flow
Fuel Cells
300 - 500 mW/cm3
~4000 mWh/cm3
Power (Energy) Density
Batteries (Zinc-Air) 1050 -1560 mWh/cm3
Batteries (rechargeable Lithium) 300 mWh/cm3 (3 - 4 V)
Solar
15 mW/cm2 - direct sun
1mW/cm2 - ave. over 24 hrs.
Vibrations 0.05 - 0.5 mW/cm3
Inertial Human Power
Acoustic Noise
3E-6 mW/cm2 at 75 Db
9.6E-4 mW/cm2 at 100 DbNon-Inertial Human Power 1.8 mW (Shoe inserts)
Nuclear Reaction
80 mW/cm3
1E6m Wh/cm3
One Time Chemical Reaction
Fluid Flow
Fuel Cells
300 - 500 mW/cm3
~4000 mWh/cm3
Energy SourcesEnergy Sources
SOURCE:SOURCE:P. Wright & S. RandyP. Wright & S. RandyUC ME Dept.UC ME Dept.
1 mW
Average
Power
Energy Scavenging [ISSCC00]Energy Scavenging [ISSCC00]
MEMS Generator
PicoJouleDSP
Power Controller
Scavenge energy from mechanical vibrations to power micro-power sensor systems
Power delivered ~ 10mW
Hardwired Fabrics enable No Power Signal Hardwired Fabrics enable No Power Signal ProcessingProcessing Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Device Min. (mW) Typ. (mW)Max. (mW) Notes
Processor + Memory 175 325 425
Running data acquisition and signal
processing
Radio (TX) 200 215 225 Transmitting at 10mW power level
Radio (RX) 170 190 200 In acquisition state, not locked
Radio (Idle) 25 40 50 Phase locked loop turned off
Sensors 123 145 160
Single channel data acquisition at max
sampling
Typical power numbers for RSC WINS Node
Hardware Baseline• Rockwell WINS is a modular stack
consisting of:Power Board StrongARM BoardRadio Board Sensor Board
• This architecture is fairly representative of other sensor nodes in the community.
• We plan to adapt this node to allow module-level power instrumentation and logging both in the lab and in the field.
Note: The processor has idle and sleep modes, but they are currently not implemented.
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Motorola StarTac Cellular Battery (3.6V)
Pico Radio Test Bed
Casing Cover
Serial Port Window
PicoNode I
Connectors forsensor boards
• Flexible platform for experimentation on networking and protocol strategies
• Size: 3”x4”x2”• Power dissipation
< 1 W (peak)• Multiple radio
modules: Bluetooth, Proxim, …
• Collection of sensor and monitor cards
PAC/C Power Roadmap
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
2000 2002 2005
10,000
1,000
100
10
1
.1
Ave
rag
e P
ow
er
(mW
)
• Deployed (5W)
• PAC/C Baseline (.5W)
• (50 mW)
(1mW)
Rehosting(10x)
-Simple Power Management-Algorithm Optimization(10x)
-System-On-Chip-Adv Power Management-Algorithms(50x)
Power Management Trade-offs in Sensor Networks
Lifetime(power)
Rapidity(latency -1)
Quality(coverage, fidelity)
Code Rate
Code Rate
Computation Energy
Code Rate
Total Energy
Lowest energy for a given BER
Communication Energy
SenseCompute
Communicate
Highly StructuredHighly Adaptive
PAC/C
Approach – Distributed Microcontroller Model w/ Local
Power Control
StrongARM CPU Module
GPS/Radio Module
Sensor InterfaceModule(s)
Image SystemModule
I2C Interface (400 kb/s)
DC/DCConverters
80C554uController
DC/DCConverters
80C554uController
DC/DCConverters
80C554uController
DC/DCConverters
80C554uController
Power Bus
Battery Pack
Single Chip Camera
Acoustic Sensors
Seismic Sensors
Magnetometers
Temperature Sensors
Other Sensors
StrongARM CPU Module
GPS/Radio Module
Sensor InterfaceModule(s)
Image SystemModule
I2C Interface (400 kb/s)
DC/DCConverters
80C554uController
DC/DCConverters
80C554uController
DC/DCConverters
80C554uController
DC/DCConverters
80C554uController
Power Bus
Battery Pack
Single Chip Camera
Acoustic Sensors
Seismic Sensors
Magnetometers
Temperature Sensors
Other Sensors
Benchmark RoadmapARL: Remote Netted Acoustic
Detection System
DSP board – 2 Motorola 96002 chips MIT AMPS system
Each node has one SA-1100
E = 3.28mJ
Ported FFT/BF C code
directly on SA-1100
Optimized Code (Floating toFixed point, etc.)
Network Computation
Partitioning and DVS
E=119.3mJ X20 E=6.01mJ X2
> X1000> X1000
Future MIT Power Aware Processor
Variable precision arithmetic
Multiple/Adaptive voltages
Hierarchical Interconnect
Leakage control techniques
…
MEM
PE
MEM
PE
MEM
PE
MEM
PE
EmbeddedEmbeddedFPGAFPGA
PicoNode II (two-chip)
ADC
DAC
Chip 2Chip 1
Custom analog
circuitry
Mixed analog/ digital
Digital Baseband processing
Fixed logic
Program-mable logic
Software running on processor
Analog RF
Protocol
Direct down-conversion front-endDirect down-conversion front-end(Yee et al)(Yee et al)
ReconfigurableDataPath
ReconfigurableDataPath
ReconfigurableState Machines
ReconfigurableState Machines Embedded uPEmbedded uP
FPGAFPGA
DedicatedDSP
DedicatedDSP
Envisioned PicoNode Platform
• Small footprint direct-down conversion R/F front end
• Digital base band processing implemented on combination of fixed and configurable data path structures
• Protocol stack implemented on combination FPGA/reconfigurable state machines
• Embedded microprocessor running at absolute minimal rates
SensorSim Hybrid Simulator
• Motivation: study sensor network deployment, protocols, applications, and power-quality trade-offs at scale in a controlled setting
• Three key capabilities– Sensor and target modeling
• Target, sensor channel, and sensor transducer characteristics– Power modeling
• Power characterization via data from instrumented platforms• Energy consumer models: radio, CPU, sensors• Energy source models: batteries• Power-quality trade-off analysis and visualization
– Hybrid simulation• selected nodes in a simulation can be “real” nodes
– currently supports only higher layers in “real” nodes• “real” applications can run on nodes in a simulation
• Current implementation based on ns simulator
SensorSim Architecturemonitor and control
hybrid network(local or remote)
Simulation Machine
Gateway Machine
ns
modified event scheduler
VR
V
VV
GUIapp
app
R
real sensor apps onvirtual sensor nodes
gateway
socketcomm
serialcomm
HS InterfaceEthernet RS232
Proxies for realsensor nodes
GUI Interface
app
SensIT Program Challenges
SURVEILLANCE:
Detection, classification and tracking of multiple simultaneous events
TWO SCENARIOS:
1. Precision distributed tracking of multiple moving targets, migrate track tables and exfiltrate reports in one second. Cue image from acoustic.
2. Fixed/Mobile mbits of data to a UAV (i.e. an image)
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Army Applications
Surveillance and monitoring– 360o field of view coverage– Excellent “wake-up” and
cueing sensor– Tactical decision aid
Detection, tracking and classification – Ground vehicles– Troop movements – Fixed and rotary wing aircraft's
Others– Detection and localization of gun fire (e.g., sniper),
artillery / mortar fire, rocket launch, etc.– Physiological monitoring of soldiers
Nino Srour
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Localization and Tracking
M1 Tank
T72 Tank4 Acoustic Sensor Location
Line of bearing from sensor
4
1
SensorArray
SensorArray
ArraySensorArray
Sensor
3
2
Acoustic sensor arrays (blue) detect bearing angle of targets(yellow), estimatelocation in real time and tracks their path as a function of time (green and red)
A test bed exists to evaluate performance of detection, tracking, identification and localization algorithms in real time against real targets. Field experiments are conducted at least once a year
Nino Srour
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Benchmark : ARL RNADS
Sensor database provided by the Army Research Laboratory
Microphone arrays are typically 4 ft – 8 ft in diameter, not restricted to a specific geometry
Acoustic Sensor Array - RNADS
All processing is done locally at the sensor arrays Target tracking occurs in real time
Courtesy of N. Srour, Army Research Lab
What’s Next?
• Refine Challenges
• Create Umbrella Research Roadmap
• What’s Available?
• What do we Co-Develop?
Robert ParkerUSC INFORMATION SCIENCES INSTITUTE
Sensor Node Model in SensorSim
Node Function Model
Network Layer
Micro Sensor Node
Applications
Power Model(Energy Consumers and Providers)
Battery Model
Radio Model
CPU Model
Sensor #1 Model
Sensor #2 Model
MAC Layer
Physical Layer
Sensor Layer
Wireless Channel Sensor Channel
NetworkProtocol Stack
SensorProtocol Stack
Middleware
Physical Layer
State Change
StatusCheck
